Relevant bibliographies by topics / One-hot Encoder

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'One-hot Encoder'

Author: Grafiati
Published: 19 February 2023
Last updated: 20 February 2023

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Journal articles on the topic "One-hot Encoder"

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SHELUHIN, OLEG I., ANNA V. VANYUSHINA, and MAKSIM S. ZHELNOV. "USE OF LATENT-SEMANTIC ANALYSIS IN PREPARATION OF DATA FOR IDENTIFICATION OF ANONYMOUS USERS BY DIGITAL FINGERPRINTS." H&ES Research 14, no. 1 (2022): 36–44. http://dx.doi.org/10.36724/2409-5419-2022-14-1-36-44.

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Digital fingerprints changings over time as a result of system, plugins, browsers, installation of various programs updates and fonts is a serious problem in the method of tracking and identifying users using a browser (Fingerprinting (FP) of a web browser). The set of parsed attributes can contain both metrical and categorical (mostly non-numeric) values, for example, parameters such as user-agent, webgl, canvas, etc. Considering this, it is required to pre-encode them for the convenience of further processing. For these purposes, artificial intelligence technologies, including the processing of text in natural languages NLP (Natural Language Processing), are widely used. The aim of the research is to analyze the peculiarities of the implementation of latent-semantic analysis (LSA) in the preparation and analysis of FP data for the identification of anonymous users. Methods. A comparative analysis of the common ways of converting categorical values of fingerprint attributes (FP) into numeric One-Hot-Encoding, Label-Encoder, LSA for identifying anonymous users with a predetermined number of possible values of categorical features is carried out. Results. The advantage of the LSA algorithm over One-Hot-Encoding, Label-Encoder is shown. The possibility of clustering implementation within the framework of the user identification problem by visualizing FP (FP) relative to hidden semantic topics using the LSA model of latent semantic analysis is shown. It is shown that with a small number of hid& den topics using the obtained vectors of objects and vectors of terms for assessing the similarity of two FPs, the proposed model allows us to confidently classify the input FP to a common topic. With the help of the obtained vectors of objects and vectors of terms for assessing the similarity of two FP objects, it becomes possible to apply various measures of cluster proximity: Euclidean distance, cosine measure, etc.
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Lv, Zhibin, Hui Ding, Lei Wang, and Quan Zou. "A Convolutional Neural Network Using Dinucleotide One-hot Encoder for identifying DNA N6-Methyladenine Sites in the Rice Genome." Neurocomputing 422 (January 2021): 214–21. http://dx.doi.org/10.1016/j.neucom.2020.09.056.

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Teng, Lin, Hang Li, and Shahid Karim. "DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation." Journal of Healthcare Engineering 2019 (December 27, 2019): 1–10. http://dx.doi.org/10.1155/2019/8597606.

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Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.
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Liu, Xiaoning, Zhihao Ke, Yining Chen, and Zigang Deng. "The feasibility of designing a back propagation neural network to predict the levitation force of high-temperature superconducting magnetic levitation." Superconductor Science and Technology 35, no. 4 (March 3, 2022): 044004. http://dx.doi.org/10.1088/1361-6668/ac55f5.

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Abstract The levitation force between the superconductor and the magnet is highly nonlinear and affected by the coupling of multiple factors, which brings many obstacles to research and application. In addition to experimental methods and finite element simulations, the booming artificial neural network (ANN) which is adept at continuous nonlinear fitting may provide another solution to predict the levitation force. And this topic has not been deeply investigated so far. Therefore, this study aims to apply the ANN to predict the levitation force, and a typical neural network applied with the back propagation (BP) is adopted. The data set with 2399 pieces of data considers nine input factors and one force output, which was experimentally obtained by several test devices. The pre-process of the data set contains cleaning, balancing, one-hot encoding (for the discrete classified variable), normalization (for the continuous variable) and randomization. A classical perception with three layers (input, hidden and output layer) is applied in this paper. And the gradient descent back propagation algorithm reduces the error by iteration. Through the assessment and evaluation of the network, a great prediction accuracy could achieve. The prediction results could well illustrate the features of force (nonlinear, hysteresis, external field dependence and type difference between the bulk and stack), which confirm the feasibility of using a BP neural network to predict the levitation force. Furthermore, the performance of the neural network is determined by the data set, especially the uniformity and balance among factors in the set. Moreover, the huge gap in the quantity of data between factors disturbs the network to make a comprehensive judgment, and in this situation, the binary one-hot encoding of the small quantity and discrete data factor is efficient, instead of the actual value of the factor, the one-hot encoded data only represent the category. Moreover, a label encoder method is adopted to distinguish the decent and ascend (decent = 1, ascent = 0) for the force hysteresis.
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Kapočiūtė-Dzikienė, Jurgita. "A Domain-Specific Generative Chatbot Trained from Little Data." Applied Sciences 10, no. 7 (March 25, 2020): 2221. http://dx.doi.org/10.3390/app10072221.

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Accurate generative chatbots are usually trained on large datasets of question–answer pairs. Despite such datasets not existing for some languages, it does not reduce the need for companies to have chatbot technology in their websites. However, companies usually own small domain-specific datasets (at least in the form of an FAQ) about their products, services, or used technologies. In this research, we seek effective solutions to create generative seq2seq-based chatbots from very small data. Since experiments are carried out in English and morphologically complex Lithuanian languages, we have an opportunity to compare results for languages with very different characteristics. We experimentally explore three encoder–decoder LSTM-based approaches (simple LSTM, stacked LSTM, and BiLSTM), three word embedding types (one-hot encoding, fastText, and BERT embeddings), and five encoder–decoder architectures based on different encoder and decoder vectorization units. Furthermore, all offered approaches are applied to the pre-processed datasets with removed and separated punctuation. The experimental investigation revealed the advantages of the stacked LSTM and BiLSTM encoder architectures and BERT embedding vectorization (especially for the encoder). The best achieved BLUE on English/Lithuanian datasets with removed and separated punctuation was ~0.513/~0.505 and ~0.488/~0.439, respectively. Better results were achieved with the English language, because generating different inflection forms for the morphologically complex Lithuanian is a harder task. The BLUE scores fell into the range defining the quality of the generated answers as good or very good for both languages. This research was performed with very small datasets having little variety in covered topics, which makes this research not only more difficult, but also more interesting. Moreover, to our knowledge, it is the first attempt to train generative chatbots for a morphologically complex language.
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Zhang, Kun, Le Wu, Guangyi Lv, Meng Wang, Enhong Chen, and Shulan Ruan. "Making the Relation Matters: Relation of Relation Learning Network for Sentence Semantic Matching." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14411–19. http://dx.doi.org/10.1609/aaai.v35i16.17694.

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Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite the effectiveness of these models, most of them treat output labels as meaningless one-hot vectors, underestimating the semantic information and guidance of relations that these labels reveal, especially for tasks with a small number of labels. To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. Specifically, we first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task for guiding R2-Net to consider more about labels. Meanwhile, a triplet loss is employed to distinguish the intra-class and inter-class relations in a finer granularity. Empirical experiments on two sentence semantic matching tasks demonstrate the superiority of our proposed model. As a byproduct, we have released the codes to facilitate other researches.
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Gaskarov, Rodion Dmitrievich, Alexey Mikhailovich Biryukov, Alexey Fedorovich Nikonov, Daniil Vladislavovich Agniashvili, and Danil Aydarovich Khayrislamov. "Steel Defects Analysis Using CNN (Convolutional Neural Networks)." Russian Digital Libraries Journal 23, no. 6 (August 4, 2020): 1155–71. http://dx.doi.org/10.26907/1562-5419-2020-23-6-1155-1171.

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Steel is one of the most important bulk materials these days. It is used almost everywhere - from medicine to industry. Detecting this material's defects is one of the most challenging problems for industries worldwide. This process is also manual and time-consuming. Through this study we tried to automate this process. A convolutional neural network model UNet was used for this task for more accurate segmentation with less training image data set for our model. The essence of this NN (neural network) is in step-by-step convolution of every image (encoding) and then stretching them to initial resolution, consequently getting a mask of an image with various classes on it. The foremost modification is changing an input image's size to 128x800 px resolution (original images in dataset are 256x1600 px) because of GPU memory size's limitation. Secondly, we used ResNet34 CNN (convolutional neural network) as encoder, which was pre-trained on ImageNet1000 dataset with modified output layer - it shows 4 layers instead of 34. After running tests of this model, we obtained 92.7% accuracy using images of hot-rolled steel sheets.
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Wu, Chunming, and Zhou Zeng. "A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing." PLOS ONE 16, no. 3 (March 1, 2021): e0246905. http://dx.doi.org/10.1371/journal.pone.0246905.

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Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.
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Weng, Weinan. "Research on the House Price Forecast Based on machine learning algorithm." BCP Business & Management 32 (November 22, 2022): 134–47. http://dx.doi.org/10.54691/bcpbm.v32i.2881.

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House price experiences some fluctuations every year, due to some potential factors such as location, area, facilities and so on. Housing price prediction is a significant topic of real estate, and it is beneficial for buyers to make strategy decisions about house dealing. There are many research on house price forecast, yet the current research cannot comprehensively compare and analyze the popular house price prediction approach. Constructing a model begins with pre-processing data to fill null values or remove data outliers and the categorical attribute can be shifted into required attributes by using one hot encoder methodology. This paper used the following five algorithms decision tree, random forest regression, Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), and extreme gradient boosting (XGBoost) this paper utilized to predict house prices and compared according to the root mean squared error. This paper found GBDT and XGBoost have more accurate prediction results compared with other algorithms. Besides, this paper found which features most affect the price of a house. In real-world applications, machine learning based housing price prediction models are utilized by banks and financial institutions to obtain better house price assessment, risk analysis and lending decisions.
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Tang, Xu, Chao Liu, Jingjing Ma, Xiangrong Zhang, Fang Liu, and Licheng Jiao. "Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing." Remote Sensing 11, no. 17 (September 1, 2019): 2055. http://dx.doi.org/10.3390/rs11172055.

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Remote sensing image retrieval (RSIR), a superior content organization technique, plays an important role in the remote sensing (RS) community. With the number of RS images increases explosively, not only the retrieval precision but also the retrieval efficiency is emphasized in the large-scale RSIR scenario. Therefore, the approximate nearest neighborhood (ANN) search attracts the researchers’ attention increasingly. In this paper, we propose a new hash learning method, named semi-supervised deep adversarial hashing (SDAH), to accomplish the ANN for the large-scale RSIR task. The assumption of our model is that the RS images have been represented by the proper visual features. First, a residual auto-encoder (RAE) is developed to generate the class variable and hash code. Second, two multi-layer networks are constructed to regularize the obtained latent vectors using the prior distribution. These two modules mentioned are integrated under the generator adversarial framework. Through the minimax learning, the class variable would be a one-hot-like vector while the hash code would be the binary-like vector. Finally, a specific hashing function is formulated to enhance the quality of the generated hash code. The effectiveness of the hash codes learned by our SDAH model was proved by the positive experimental results counted on three public RS image archives. Compared with the existing hash learning methods, the proposed method reaches improved performance.
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Dissertations / Theses on the topic "One-hot Encoder"

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Haq, Ikram. "Fraud detection for online banking for scalable and distributed data." Thesis, Federation University Australia, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171977.

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Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric data. So, there is a need to use techniques e.g. One-hot encoding (OHE) to transform categorical features to numerical features, however OHE has challenges including the sparseness of transformed data and that the distinct values of an attribute are not always known in advance. Efficient feature engineering can improve the algorithm’s performance but usually requires detailed domain knowledge to identify correct features. Techniques like Ripple Down Rules (RDR) are suitable for fraud detection because of their low maintenance and incremental learning features. However, high classification accuracy on mixed datasets, especially for scalable data is challenging. Evaluation of RDR on distributed platforms is also challenging as it is not available on these platforms. The thesis proposes the following solutions to these challenges: • We developed a technique Highly Correlated Rule Based Uniformly Distribution (HCRUD) to generate highly correlated rule-based uniformly-distributed synthetic data. • We developed a technique One-hot Encoded Extended Compact (OHE-EC) to transform categorical features to numeric features by compacting sparse-data even if all distinct values are unknown. • We developed a technique Feature Engineering and Compact Unified Expressions (FECUE) to improve model efficiency through feature engineering where the domain of the data is not known in advance. • A Unified Expression RDR fraud deduction technique (UE-RDR) for Big data has been proposed and evaluated on the Spark platform. Empirical tests were executed on multi-node Hadoop cluster using well-known classifiers on bank data, synthetic bank datasets and publicly available datasets from UCI repository. These evaluations demonstrated substantial improvements in terms of classification accuracy, ruleset compactness and execution speed.
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Book chapters on the topic "One-hot Encoder"

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Ul Haq, Ikram, Iqbal Gondal, Peter Vamplew, and Simon Brown. "Categorical Features Transformation with Compact One-Hot Encoder for Fraud Detection in Distributed Environment." In Communications in Computer and Information Science, 69–80. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6661-1_6.

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Wu, Zhuo, Tsuyoshi Migita, and Norikazu Takahashi. "Element-Wise Alternating Least Squares Algorithm for Nonnegative Matrix Factorization on One-Hot Encoded Data." In Communications in Computer and Information Science, 342–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63823-8_40.

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Potluri, Rajasekhara Mouly, and Sophia Johnson. "The Influence of Risks Associated With Organizational Factors on Women's Professional Growth During COVID-19 in the UAE." In Advances in Logistics, Operations, and Management Science, 224–43. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5279-0.ch012.

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The core objective of this chapter is to explore the risks associated with organizational factors influencing the professional growth of women in the United Arab Emirates (UAE) during the pandemic. The collected data were summarized and coded using software R Studio, and the variables were encoded and reduced using the one-hot encoding method and principal component analysis (PCA). The researchers identified that organizational and situational factors have a high degree of impact on women's professional development, which creates a significant effect of discontent over the mindset of women employees even in uncertain conditions. The study covers women employees working only in two emirates, Dubai and Sharjah. It includes telecom, banking, education, and other governmental and non-governmental organizations. This chapter is valuable to all the policymakers of the entire corporate sector and government authorities to set the right things by observing diverse organizational factors that influence women employees.
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Conference papers on the topic "One-hot Encoder"

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Daund, Chinmay, Siddharath Bohra, Neel Nimbalkar, and Sujata Oak. "Comprehensive Analysis based on One Hot Encoder for Brain Tumor Detection and Classification." In 2022 International Conference on Intelligent Technologies (CONIT). IEEE, 2022. http://dx.doi.org/10.1109/conit55038.2022.9847686.

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Prossinger, Hermann, Jakub Binter, Kamila Machová, Daniel Říha, and Silvia Boschetti. "Machine Learning Detects Pairwise Associations between SOI and BIS/BAS Subscales, making Correlation Analyses Obsolete." In Human Interaction and Emerging Technologies (IHIET-AI 2022) Artificial Intelligence and Future Applications. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100902.

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We use AI techniques to statistically rigorously analyze combinations of query responses of two personality-related questionnaires. One questionnaire probes aspects of a participant’s character (SOI) and the other avoidance of aversive outcomes together with approaches to goal orientated outcomes (BIS/BAS). We use one-hot encoding, dimension reduction with a neural network (a seven-layer auto-encoder) and two clustering algorithms to detect associations between the twelve combinations of SOI and BIS/BAS groups. We discover that for most combinations more than one association exists. Traditional, fallacious statistical methods cannot find these outcomes.
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Macchiarulo, Luca, and Malgorzata Marek-Sadowska. "Wave-steering one-hot encoded FSMs." In the 37th conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/337292.337440.

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Zhang, Jiani, Xingjian Shi, Shenglin Zhao, and Irwin King. "STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/592.

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We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in https://github.com/jennyzhang0215/STAR-GCN.
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Wohl, Peter, A. Waicukauski, and Sanjay Patel. "Automated Design and Insertion of Optimal One-Hot Bus Encoders." In 25th IEEE VLSI Test Symmposium. IEEE, 2007. http://dx.doi.org/10.1109/vts.2007.18.

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Oh, Seung Hyub, Seung Jin Song, and Yong Shik Hong. "An Experimental Facility Design to Determine Rotordynamic Coefficients due to Tip Clearance Asymmetry in Axial Turbines." In ASME 1997 Turbo Asia Conference. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/97-aa-018.

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Many liquid rocket engines for space launch vehicles use high performance turbopumps to deliver fuel and oxidizer to the thrust chamber. Such turbopumps have been plagued by rotordynamic instability caused by non-axisymmetric turbine tip clearance. Yet, there is dearth of experimental data on this phenomenon, and reliable damping data are non–existent. This paper describes a new experimental facility designed and built to obtain aerodynamic stiffness and damping forces in an unshrouded turbine with a whirling rotor. The experimental facility consists of an air supply system, single turbine stage test section, instrumentation, and auxiliary systems. The test turbine is a 1: 1 replica of an unshrouded turbine stage used in an actual turbopump. This impulse turbine has a design flow coefficient of 0.76 and work coefficient of 5.9. To measure aerodynamic stiffness forces, the turbine casing is mounted so that it is eccentric relative to the turbine rotor which is concentric with the turbine shaft. Thus, static turbine tip clearance asymmetry is obtained. To obtain aerodynamic damping forces, the turbine casing is mounted concentric relative to the turbine shaft, and the turbine rotor is mounted eccentric relative to the shaft. Thus, synchronous forward whirling condition, or dynamic turbine lip clearance asymmetry, is simulated. Currently, the mean tip clearance is 2% of the rotor blade span, and the specified eccentricity is 1% of the rotor blade span. This test rig facilitates acquisition of both stiffness and damping rotordynamic forces due to turbine aerodynamics. In contrast, past experiment designs required two separate drives — one for rotation and second for whirling motion — to simulate whirling turbine. Therefore, the new facility offers a comparably inexpensive and reliable alternative testing method. Instrumentation consists of pilot static probes, thermocouples, and optical encoder (tachometer) to monitor turbine operating conditions. Proximity probes are used to measure tip clearance. Steady pressure data are obtained through a set of static pressure taps on the casing. Dynamic pressure data are obtained through flush-mounted, Kulita unsteady pressure transducers. Finally, steady and dynamic velocity data are acquired via 2–D hot wire probes. Thus, steady and unsteady flow fields in turbines with statically offset rotor and whirling rotor, respectively, can be measured.
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Liang, Xiaoyuan, Martin Renqiang Min, Hongyu Guo, and Guiling Wang. "Learning K-way D-dimensional Discrete Embedding for Hierarchical Data Visualization and Retrieval." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/411.

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Traditional embedding approaches associate a real-valued embedding vector with each symbol or data point, which is equivalent to applying a linear transformation to ``one-hot" encoding of discrete symbols or data objects. Despite simplicity, these methods generate storage-inefficient representations and fail to effectively encode the internal semantic structure of data, especially when the number of symbols or data points and the dimensionality of the real-valued embedding vectors are large. In this paper, we propose a regularized autoencoder framework to learn compact Hierarchical K-way D-dimensional (HKD) discrete embedding of symbols or data points, aiming at capturing essential semantic structures of data. Experimental results on synthetic and real-world datasets show that our proposed HKD embedding can effectively reveal the semantic structure of data via hierarchical data visualization and greatly reduce the search space of nearest neighbor retrieval while preserving high accuracy.
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Kumar, Ankit, and Amit Priyadarshan. "Well-Integrity Assessment Across Different Geological Areas by Deriving Insights from Complex Knowledge Base." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211240-ms.

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Abstract Over the lifetime of multiple wells, in different fields, data produced from integrity assessment of the casing and mechanical parts of oil and gas wells accumulates to huge amounts and diversity. Knowledge derived from these test records can help in integrity assessment of other wells and explain contributing factors. This paper presents application of a unique Deep-Learning algorithm, to automate well-integrity assessment by extracting entire knowledge from an existing database of millions of integrity-tests, using a complex Deep Neural Network (DNN), and transfer this knowledge into another simple DNN model to provide an explainable integrity assessment and contributing factors for end user. Herein, we present a two-phase algorithm-development process. It uses values of annular pressure, maximum allowable pressure, production annulus shut-in pressure, surface wellhead emission rate, corrosion etc. from 105301 oil and gas wells. Firstly, a complex Deep Neural Network (DNN) highly regularized with drop-outs and equivalent to summation of exponential number of models, extracts knowledge-representation, i.e., mapping between quantification of mechanical properties, their evolution and factors contributing to these properties, from well-integrity tests records of all the fields. In the second phase, the knowledge-representation learned by the complex DNN is passed on to the simple DNN. It extracts well-specific information from the knowledge-representation, with its two objective functions and provides an explainable integrity assessment for end users to make better decisions. First DNN with thousands of trainable parameters is cumbersome, unexplainable and has very slow execution speed. Second DNN with only a few hundred parameters outputs one-hot encoded target vector of values for Sustained Casing Pressure (SCP), Casing Vent Flow (CVF) and corrosion, to quantify integrity of a well. These vectors and soft probability from knowledge representation of the first DNN, combines in the first objective, to ensure transfer of entire knowledge from the first DNN to the second DNN. Second objective function performs optimization between calculated probability of SCP, CVF and corrosion, and corresponding truth values in a very small training set. Second DNN fails to perform if it does not use knowledge-transfer from the first DNN. With the second objective, the second DNN achieves an accuracy of 93%. Development-database consists of records of well-integrity assessments performed in Raton, San-Juan, Denver-Julesburg, Appalachian, Permian and Piceance basin of Colorado, New Mexico and Pennsylvania, between 1991 and December, 2020. Proposed experiments were performed on Nvidia RTX 2060 SUPER 2x8GB GPU using deep learning framework. Novelty of this paper lies in demonstration of one of the initial applications of knowledge-distillation, a deep-learning algorithm, to automate well-integrity assessment. It is a unique method of transferring knowledge-representation learned from a huge database by a complex DNN, to a simpler DNN, for explainable and fast assessment.
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